Course

Course Summary
Credit Type:
Course
ACE ID:
MLS-0103
Organization's ID:
APL-3007
Organization:
Microsoft Corporation
Location:
Online
Length:
5-8 hours
Dates Offered:
Credit Recommendation & Competencies
Competency Framework Statement
AI Competency Framework Understanding Data: 1.1 Employ different types of data and their representations
AI Competency Framework Understanding Data: 1.2 Analyze typical uses of data in machine learning (ML) and AI
AI Competency Framework Data Handling and Manipulation: 3.1 Prepare data for use in an ML or AI project
AI Competency Framework Data Handling and Manipulation: 3.2 Manipulate data
AI Competency Framework Core Language Skills: 1.1 Write code using proper syntax and structure
AI Competency Framework Core Language Skills: 1.2 Incorporate libraries
AI Competency Framework Core Language Skills: 1.3 Improve code performance
AI Competency Framework Data Reprocessing: 1.1 Prepare features for use in supervised or non-supervised learning tasks
AI Competency Framework Supervised Learning: 2.1 Manage a supervised learning framework
AI Competency Framework Unsupervised Learning: 3.1 Manage an unsupervised learning framework
AI Competency Framework Data Storage: 1.1 Manipulate data stored in files
AI Competency Framework Data Storage: 2.1 Manipulate data stored in databases
AI Competency Framework Cloud Computing: 3.1 Use different types of cloud architectures
AI Competency Framework Tools: 4. Find documentation for the tool
AI Competency Framework Tools: 3. Configure the tool
AI Competency Framework AI Fundamentals: 1.1. Apply technical concepts based on hybrid AI knowledge
Description

Objective:

The course objective is to demonstrate ability to train and deploy machine learning models with Azure Machine Learning.

Candidates for this credential should be familiar with Azure services, have experience with Azure Machine Learning and MLflow, and have experience performing tasks related to machine learning by using Python.

Learning Outcomes:

  • Set up a development environment in Azure Machine Learning
  • Prepare data for model training
  • Create and configure a model training script as a command job
  • Manage artifacts by using MLflow
  • Deploy a model for real-time consumption

General Topics:

  • Set up a development environment in Azure Machine Learning
  • Prepare data for model training
  • Create and configure a model training script as a command job
  • Manage artifacts by using MLflow
  • Deploy a model for real-time consumption
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Practical Exercises

Methods of Assessment:

  • Other
  • Performance based test

Minimum Passing Score:

70%
Supplemental Materials